549 results about "Relation graph" patented technology

According to the research and development document processing method and device provided by the embodiment of the invention, unified processing of text, voice and image information is realized by constructing the multi-mode document analysis engine. Different types of data are converted into standardized feature representations through cross-modal preprocessing and a semantic alignment network. The system adopts a deep learning model to identify research and development elements, establishes a multi-modal relation graph, and realizes intelligent extraction and correlation analysis of various types of information in research and development documents. According to the method, the defects of the traditional technology in the aspects of multi-modal information fusion and knowledge system construction are effectively overcome, powerful support is provided for research and development process management and knowledge asset accumulation, and the standardized management level of research and development documents is remarkably improved.

The invention discloses an intelligent target allocation method and system based on deep reinforcement learning, particularly relates to the field of multi-agent systems, and is used for solving the real-time decision problem under multi-dimensional constraints in a dynamic confrontation simulation scene. Time, space and resource constraints are decomposed into independent models, and the decision complexity of a multi-agent system in a dynamic confrontation scene is reduced; by means of adaptive Monte Carlo sampling, prediction perception path planning and dynamic threshold value resource allocation, diversified high-quality candidate schemes are quickly generated; when the environment changes, the multi-layer influence propagation network and the spatial topological relation graph are used for positioning an affected agent, and scheme fragments are generated through local adjustment, so that global re-calculation is avoided, the overhead is reduced, and real-time response is ensured; meanwhile, an efficient and reliable optimal solution is screened out through resource delay coupling and path efficiency comprehensive evaluation in combination with multi-objective optimization and task consistency verification.

The invention provides an industrial chain atlas modeling method and system based on deep learning, and relates to the technical field of industrial chain analysis and modeling, and the method comprises the steps: collecting multi-source heterogeneous data in an industrial chain, including demand information, resource states, process features and operation and maintenance data, and preprocessing the multi-source heterogeneous data to form a standardized data set; and performing semantic vectorization processing on the text type data in the standardized data set, and performing node embedding on the multi-relation graph to generate entity low-dimensional representation fusing text and structural features. According to the method, by integrating multi-source heterogeneous data, fusing text and structural features, constructing an industrial chain knowledge graph and performing dynamic optimization, accurate adjustment of production line configuration and task allocation strategies is realized, meanwhile, the industrial trend is effectively predicted, risk paths are analyzed and resource bottlenecks are identified in combination with a risk propagation model, a scientific basis is provided for industrial chain decision-making, and the risk propagation model has a wide application prospect. And the operation efficiency and stability of an industrial chain are improved.

The invention relates to the technical field of archive information management, and provides an archive information extraction management method and system based on multi-modal learning, and the method comprises the steps: collecting multi-modal archive data, carrying out the preprocessing of the multi-modal archive data, and obtaining the processed multi-modal archive data; performing feature extraction on the multi-modal archive data, and obtaining an archive comprehensive feature vector through weight distribution and feature alignment; based on a preset archive knowledge graph, performing multi-modal entity recognition on the archive comprehensive feature vector, constructing an archive relation graph, and performing iterative calculation through a graph neural network to obtain an archive association rule; updating a preset archive knowledge graph according to the archive association rule to obtain an archive enhanced knowledge graph, and performing iterative optimization on the archive association rule to form and store an archive management strategy network; and constructing an archive information index system based on the archive enhanced knowledge graph and the archive management strategy network. According to the invention, efficient management and accurate retrieval of archive information are realized.

The invention discloses an energy consumption model intelligent construction system and method based on artificial intelligence, and relates to the technical field of artificial intelligence, and the system comprises an Internet of Things multi-source data collection module, a data cleaning and space-time calibration module, a multi-source data semantic fusion module, a dynamic energy consumption relation graph construction module, an intelligent decision engine module and an edge-cloud collaborative deployment module. According to the method, multi-source data are fused through a Transform multi-head self-attention mechanism, a dynamic energy consumption relation graph is constructed by using a graph neural network, and dynamic modeling and intelligent regulation and control of energy consumption are realized in combination with an edge-cloud hierarchical decision architecture; the method comprises the steps of data acquisition and standardization, cleaning calibration, semantic fusion, graph modeling, hierarchical decision making and collaborative execution. According to the method, the problems of insufficient data integration and model staticization of a traditional system are solved, the accuracy, real-time performance and global optimization capability of energy consumption management are improved, the method is suitable for scenes such as intelligent buildings, the energy efficiency is remarkably improved, and the data security is guaranteed.

The invention relates to the technical field of defect detection, in particular to a cross-process defect root cause tracing method and system. According to the method, the data feature matrix covering multiple dimensions is formed by integrating the process parameters, the equipment state and the quality detection information, so that the performance evaluation of each process is more comprehensive, the interaction and influence paths among the processes can be clearly described by constructing the process relation graph, and the performance evaluation efficiency is improved. Meanwhile, basic data support is provided for quantifying the relation between the procedures through introduction of procedure attenuation factors, the shortest propagation path and the propagation probability of the defects can be accurately recognized by analyzing a procedure relation graph, root cause tracing of the cross-procedure defects becomes systematized in combination with construction of a knowledge graph, and the defect tracing efficiency is improved. The knowledge graph not only can effectively integrate and display data, but also is convenient for quickly positioning problems, and by utilizing an adaptive correlation analysis technology, the system can intelligently adjust an analysis model and a path and continuously optimize a defect detection and tracing process when facing new data.

The invention provides an abnormal behavior recognition method and system based on multi-dimensional data analysis, and the method comprises the steps: carrying out the cleaning and standardization processing of structured data and unstructured data, obtaining a data set in a unified format, employing a natural language processing technology, processing the document text in the data set, extracting the entity, relation and behavior information, and carrying out the recognition of abnormal behaviors. Generating a structured feature vector; constructing behavior statistics, time sequence and association features by using the feature vectors and the structured data, selecting a feature combination by adopting a sampling algorithm, establishing an entity relation graph according to association data in a constructed multi-dimensional feature set, calculating association strength among entities, and optimizing the entity relation graph according to the association strength to establish a network structure model; a feature weight system is set according to the model and a multi-dimensional feature set, a multi-dimensional abnormal score is calculated by using a pruning tensor structure measurement method, an abnormal score is comprehensively calculated by using a low-rank tensor recovery technology, and an early warning list is output, so that the recognition accuracy and efficiency can be improved.

The invention relates to a water quality prediction method and system based on gating residual enhancement and feature fusion, and belongs to the technical field of water environment intelligent analysis and deep learning. Taking each water quality index as a node of the graph, and constructing two complementary variable relation graph structures by utilizing a Pearson's correlation coefficient and mutual information; respectively inputting the two graph structures into a graph convolutional network, extracting deep dependency features among indexes, and splicing and fusing the deep dependency features. A multi-head attention mechanism is used as a trunk to extract global time dependence, a GRU network is introduced to extract local time sequence features, GRU output is used as an adjustable residual term to be injected into the attention trunk through a residual gating mechanism, self-adaptive enhancement of local dynamic features is achieved, and finally a self-adaptive fusion mechanism is introduced to generate comprehensive representation. According to the method, the complex dependency relationship between the water quality indexes and the time dynamic evolution process can be modeled in a collaborative manner, the response capability to key local change and sudden change events is remarkably enhanced, and the accuracy and robustness of water quality prediction are improved.

The invention provides a project management software document uploading method based on OCR and a large language model, and relates to the technical field of large language models, and the method comprises the steps: receiving a project document picture, and carrying out OCR recognition through a multi-scale feature pyramid and an attention-enhanced convolutional neural network; inputting the text information into a pre-trained large language model for classification and key field extraction; determining a data partitioning strategy according to the item type to create a storage space; establishing a multi-dimensional index structure to store project information and analyzing a content logic relationship; and generating and displaying a project information relation graph. According to the invention, intelligent identification, classification and correlation analysis of the project documents are realized, and the project management efficiency and the data utilization value are improved.

The invention relates to the technical field of database management, in particular to a two-way linkage database table and supervision submission form field synchronous construction method which is applied to a supervision data submission scene. According to the scheme, the method comprises the steps that physical structure metadata of a database table and definition metadata of a supervision submission form are obtained, a semantic vector set is formed by combining metadata semantic analysis and semantic coding, and a bidirectional mapping relation graph is constructed through relation weaving; constructing a linkage propagation path according to a field change event, realizing adaptive bidirectional structure adjustment, generating a field synchronous construction scheme, and driving bidirectional mapping relation graph evolution optimization through feedback learning; according to the method, the bidirectional mapping relation graph and self-adaptive bidirectional structure adjustment are creatively combined, and efficient and self-adaptive bidirectional field synchronous construction is realized.

The invention discloses a park management method based on digital twinning, particularly relates to the field of park operation, and is used for solving the problem that a multi-source event is difficult to stably chain under the conditions of compensation delay and evidence gap and causes misalignment of disposal triggering. Comprising the following steps of: registering an event in a pool to generate an event number and writing the event number into a platform arrival moment; screening and mapping an anchor point event to a park object identifier and a process stage identifier to generate a management token; checking a stage sequence and an evidence item requirement set according to an event chain rule table in event chain construction and generating a conflict point list; the delay portrait library generates a credible mark when a source is generated and forms a reverse sequence violation spectrum; the digital twin object relation graph forms an influence domain span mark; the closed table is returned to obtain a disposal convergence guarantee level; the cost-sensitive decision tree outputs a disposal template identifier, and the disposal template table generates a management action instruction and a trigger voucher and records an instruction acceptance identifier; and returning, comparing and freezing the exception management token, and updating the event chain rule table and the delay portrait library.

The invention relates to the technical field of databases, and discloses a multi-type database performance optimization and operation and maintenance management method and system.The multi-type database performance optimization and operation and maintenance management method comprises the steps that real-time performance indexes of a heterogeneous database cluster are collected, and a noise reduction data set is obtained; generating a cross-library performance coupling degree matrix through the resource competition coupling degree and the data dependence coupling degree; constructing a coupling relation graph; identifying a bottleneck node set based on the coupling relation graph and the abnormal level; generating an optimization strategy; and performing strategy conflict identification according to the optimization strategy, generating a processing strategy, and executing the processing strategy optimization strategy. Through the improved weighting centrality algorithm, the core bottleneck node which has the greatest influence on the whole cluster is accurately identified, a differential optimization strategy is adopted according to the database type, a perfect strategy conflict detection and avoidance mechanism is established, mutual interference in the optimization process is effectively prevented, the optimization success rate is improved, and the optimization efficiency is improved. And the optimization time is shortened.

The invention, which relates to the technical field of ecological environment monitoring and intelligent management, discloses a real-time monitoring and early warning management system based on an ecological environment, comprising a multi-modal data acquisition module for acquiring and preprocessing multi-medium environment data, a multi-medium risk conduction modeling module for constructing a directed graph, and a dynamic modeling risk conduction process. The cross-medium risk conduction chain effect evaluation system has the advantages that the problem of cross-medium risk conduction chain effect evaluation deficiency is effectively solved, multi-modal data provides a data basis, the modeling module constructs a directed conduction relation graph, and the model construction module constructs a three-dimensional model and simulates pollutant migration and transformation and risk effects, and the intelligent early warning module generates early warnings, so that the cross-medium risk conduction chain effect evaluation system has the advantages that the cross-medium risk conduction chain effect evaluation deficiency problem is effectively solved; a graph neural network is used for dynamic modeling, a conduction path is presented, a digital twin engine simulates pollutant migration and transformation and a risk conduction effect, an intelligent early warning module carries out multi-level evaluation and timely early warning, a decision intervention module generates an optimal intervention scheme, and the system realizes accurate evaluation and effective response of cross-medium risk conduction.

The invention discloses an electric power production data private network security situation awareness system and method. The method comprises the following steps: 1) acquiring security data of an electric power production data private network in real time; 2) standardizing the acquired data; 3) constructing a power grid entity relation graph, and fusing the standardized data obtained in the step 2) into the graph; 4) constructing an attack graph, and associating the attack graph with the power grid equipment entity relationship graph; 5) combining the power grid entity relation graph with the attack graph, performing deep correlation analysis, and identifying potential attacks in the power grid; 6) generating a grade risk alarm according to an identification result in the step 5), and triggering corresponding countermeasures; and 7) simulating the defense measures in the step 6), and optimizing and adjusting the defense countermeasures according to a feedback result. According to the method, the multi-source data is combined with the dynamic knowledge graph technology, real-time correlation analysis of the power grid entity relationship and the attack behavior is supported, and the recognition capability of novel attacks is remarkably improved.

The invention relates to the technical field of data processing and analysis, and discloses a cost consultation data arrangement and analysis system based on a cloud platform, and the system comprises a data collection module which is used for obtaining and standardizing original cost data; the data clustering module is used for clustering data according to specific dimensions and extracting key feature vectors; the strategy generation module is used for constructing candidate analysis strategies; the strategy optimization module is used for screening an optimization strategy; and the execution engine module processes the data according to the optimization strategy to generate an analysis result. In addition, a data verification module is arranged to verify results, a dynamic adjustment module is arranged to monitor data changes, and a knowledge graph module is arranged to construct an entity relation graph. The system can solve the problems of data source heterogeneity, single analysis strategy and the like in cost data processing, realizes efficient data processing and accurate analysis, ensures that the result is accurate and reliable, and improves the data processing and analysis level of the cost consultation industry.

The invention relates to the technical field of data fusion, and discloses an animal scene-oriented adaptive multi-modal data fusion method, which comprises the following steps of: extracting spatio-temporal characteristics from multi-source heterogeneous data such as visual sense, auditory sense and physiological sensing, constructing an animal-environment-group ternary spatio-temporal relation graph, and constructing an animal-environment-group ternary spatio-temporal relation graph; a pilot frequency sampling problem is solved through an adaptive interpolation algorithm, cross-modal projection alignment is completed in a public semantic space, unified space-time representation is output, and confidence coefficient weight is dynamically calculated based on uncertainty measurement of each modal feature. According to the method, accurate alignment of multi-modal data is realized through the cross-modal space-time attention network, the multi-modal feature alignment error is reduced compared with that of a traditional LSTM method, the training data volume of a federated element migration reinforcement learning framework is reduced compared with that of a traditional migration learning method, and the cross-species generalization performance of the model is improved. A multi-level causal inference engine quantitatively reveals causal association between environmental factors and animal diseases, and in combination with a dynamic decision tree visualization technology, the decision recognition degree is improved.

The invention discloses an enterprise big data security early warning method based on anomaly detection, and the method comprises the following steps: S1, collecting original data, and carrying out the format unification and structure standardization processing; s2, preprocessing is carried out, and feature vectors are constructed; s3, a behavior entity relation graph is constructed, a graph attention network is adopted for training, and structural features in a normal behavior mode are learned; s4, calculating the deviation degree between the current behavior and the normal behavior; s5, reconstructing the feature vector, measuring the deviation degree between the current behavior and the standard behavior distribution by using a mahalanobis distance, and calculating the posterior anomaly probability of the behavior through a Bayesian updating mechanism; and S6, evaluating the risk level of the current behavior according to the posterior anomaly probability, and generating a corresponding early warning event. According to the method, the graph attention network and the Bayesian self-coding technology are fused, enterprise behavior anomaly detection and grading early warning are achieved, and the method has the advantages of being high in recognition precision, high in self-adaption and timely in response.

The invention discloses a network space map surveying and mapping method and system based on multi-source data fusion, and the method comprises the steps: obtaining a multi-source data set in a unified format based on network flow data, equipment information data and geographic position data; obtaining a network asset entity and an incidence relation graph thereof through entity identification and correlation analysis based on the multi-source data set with the uniform format; obtaining a network space three-dimensional map model through three-dimensional space mapping and visual rendering based on the network asset entity and the association relationship map thereof; based on the network space three-dimensional map model, performing dynamic updating according to the accessed real-time data flow to obtain real-time network space situation data; and obtaining a network security risk assessment result through anomaly detection and threat identification based on the real-time network space situation data. According to the invention, visual display and security situation awareness of the network space are realized, and a brand new decision support tool is provided for network security management.

The invention provides a false news detection method based on multi-modal information fusion and a medium. The method comprises the following steps: firstly, acquiring news texts, images, users and comment information, and constructing a social relation graph containing news nodes, user nodes and comment nodes; low-similarity connection is removed through node similarity calculation, potential connection is deduced, and an improved social relation graph is generated. Performing feature coding on the text and the image by using the pre-training model to generate a text feature vector, an image feature vector and a social relation feature vector; and text and image entity embedding enhancement feature vectors are extracted. And mapping the enhanced text, image and social relation feature vectors to a common semantic space, and realizing modal alignment by optimizing mean square error loss to obtain cross-modal alignment feature vectors. And finally, fusing the multi-modal information through a common attention mechanism, generating a multi-modal fusion feature vector, inputting the multi-modal fusion feature vector into a classifier, and outputting true and false news labels. According to the method, the precision and robustness of false news detection are improved.

The invention discloses a software development result traceability analysis system based on version control, and relates to the technical field of intelligent software analysis. According to the method, the non-tampering property of code submission records is ensured through a block chain technology, a credible basis is provided for traceability, the code semantic analysis module generates a data set containing semantic association in combination with a clustering algorithm and weighted calculation, and the function influence positioning module constructs a function association graph by applying a graph neural network algorithm, so that the traceability is improved. An influence path of code change on system functions is automatically identified, the time cost of complex project function influence positioning is remarkably reduced, an evolution path generation module analyzes and tracks a code evolution path in combination with a time sequence, and a core module identification module clusters key nodes and positions a core function module. And the time-tracing source data storage module generates a visual relation graph and a queried data set, so that the whole-process management from code submission to result tracing is realized.

The invention discloses a method for realizing element positioning in an RPA system, which comprises the following steps of: 1, obtaining an original request input by a user and a current webpage of the user, and obtaining a view relation graph by utilizing the webpage; 2, recognizing similar elements according to the view relation graph; splitting the original request into a condition judgment instruction or a loop execution instruction; step 3, acquiring element coordinates for the loop execution instruction based on the view relation graph; step 4, disassembling the condition judgment instruction into an element query; then recalling visible elements to be recognized in the similar elements; and step 5, for the visible elements to be identified, acquiring element coordinates by using the view relation graph or the multi-modal large model. According to the invention, element positioning can be rapidly realized in the RPA working process, and the misjudgment condition is reduced at the same time.

The invention provides a multi-source network equipment fault mode identification and positioning method and system, and relates to the technical field of computer fault diagnosis, and the method comprises the steps: collecting equipment hardware data and software data, generating a fault feature vector, collecting adjacent equipment information, and constructing a communication relation graph and a link abnormity probability distribution graph; determining fault source equipment and probability through correlation analysis; according to the method, the processing instruction is generated, and the baseline data is updated, so that the computer network fault can be accurately identified and quickly positioned, the fault processing efficiency is improved, and the network maintenance cost is reduced.

The invention relates to the technical field of report generation, in particular to an enterprise business report generation method and system based on a large model. The method comprises the following steps: acquiring component operation state data and production plan data of industrial equipment; performing master-slave relation division on internal components of the equipment according to the component operation state data, designating a driving device as a master component, designating a transmission component and an execution component as slave components, and constructing a component transfer chain relation graph; monitoring operation state parameters of the main component in the component transmission chain relation graph; extracting the master component of which the fluctuation of the operation state parameter exceeds a preset threshold value, and measuring the response parameter change condition of the connected slave component; and generating abnormal transmission confirmation data according to the response parameter change condition. According to the invention, based on abnormal transmission characteristics between components, early fault identification and propagation path tracking are realized, and a problem source is found before fault expansion.

The invention discloses a risk assessment method and system based on topology analysis, and relates to the technical field of risk assessment, and the method comprises the following steps: analyzing a communication relation between nodes, extracting a plurality of propagation paths between any two nodes, and forming a path set; mapping the path set based on the risk transmission direction and the path attribute, and constructing a multi-path propagation relation graph; based on the multi-path propagation relation graph, identifying a risk aggregation node with multi-path risk input, and fusing multi-path attributes to calculate a comprehensive risk value of the node; and generating network risk distribution based on the comprehensive risk value in combination with the operation state data. According to the method, the multi-path propagation topology model is constructed, and the risks on multiple paths are subjected to convergence analysis and comprehensive quantification in combination with the node operation state and the path interaction characteristics, so that the problem that the overall risk of the network is underestimated only by depending on a single dominant path in the prior art is solved.

The invention provides an scRNA-seq data clustering method, system and device based on ZINB distribution and graph attention. The scRNA-seq data clustering system mainly comprises three core modules: a ZINB auto-encoder, which is used for modeling scRNA-seq data based on zero-expansion negative binomial distribution, generating robust potential representation through a denoising auto-encoder, and accurately capturing sparsity, excessive discreteness and shedding events of gene expression; the residual image attention auto-encoder is used for constructing a cell relation graph by using a Pearson correlation coefficient, dynamically learning a neighborhood weight in combination with a multi-head attention mechanism, retaining original features through residual connection, and relieving the excessive smoothness problem of image convolution; and a deep clustering model is self-optimized: target distribution and soft label distribution are minimized through KL divergence, and end-to-end joint optimization of embedded learning and clustering is realized.

The invention discloses an enterprise innovation risk assessment system based on virtual-real fusion common knowledge learning, and the system comprises a data collection and preprocessing module which is used for collecting and preprocessing a real operation standardized data set; the simulation environment construction and disturbance generation module is used for constructing a virtual simulation environment and generating a virtual data set; the semantic alignment and fusion module is used for performing semantic alignment and weighted fusion to generate a virtual-real fusion data set; the risk relation graph construction module is used for constructing an enterprise innovation risk relation graph; the multi-order structure embedding module is used for executing multi-order graph structure embedding; the common knowledge migration module is used for extracting a common feature structure and executing migration adaptation; and the risk assessment module is used for carrying out risk assessment. According to the method, enterprise atlas and multi-semantic structure embedding are fused, cross-enterprise risk migration assessment is achieved, and the method has the advantages of being high in expression ability, high in adaptability and accurate in assessment.

The invention relates to the technical field of computer networks, in particular to a network risk processing method and system based on a graph. The method comprises the following steps: performing network entity model construction on a target network to obtain a target network entity model; performing network entity graph modeling on the target network based on the target network entity model to obtain a network entity relationship graph; performing dynamic graph updating on the network entity relation graph to obtain an updated network entity relation graph; carrying out risk propagation source node determination on the updated network entity relation graph to obtain a risk propagation source node set; and performing global risk assessment on the target network according to the risk propagation source node set based on the updated network entity relationship graph to obtain a network global risk assessment result. According to the invention, the efficiency and accuracy of network risk assessment are improved.

The invention provides a multi-view structure learning method based on multi-expert cooperation, and relates to the technical field of graph neural networks and multi-view learning, and the method comprises the steps: reconstructing initial multi-view data, and obtaining multi-view structure data; respectively training the single-view expert model and the shared expert model by utilizing a first loss function and a second loss function based on the multi-view structure data to obtain a trained single-view expert model and a trained shared expert model; constructing a collaborative decision model by using the trained single-view expert model and the trained shared expert model; training the collaborative decision model by using a third loss function to obtain a trained collaborative decision model; and analyzing the graph data by using the trained collaborative decision model to obtain a joint decision result, and completing learning of the multi-view structure. According to the method, the problems of large structural noise interference, isolated expert model information and insufficient node classification accuracy when an existing graph neural network processes multi-relation graph data are solved.

The invention discloses a software development management method based on big data, and relates to the technical field of software development management, and the method comprises the steps: collecting multi-source development data, transmitting the multi-source development data to a data lake in real time through a distributed message queue, and generating a structured data stream with a timestamp and a time sequence relation graph; performing Monte Carlo sampling on the three-dimensional radiation field model of the neural radiation field and the causal influence graph, simulating the cascade influence submitted by the current code, and outputting a risk value and a dependency chain; loading a neural radiation field three-dimensional code space model, a node risk value and a dependency chain, dynamically rendering a causal influence path, and generating a visual risk report; and generating a code optimization task according to the visual risk report, submitting an optimized code version to a code library, and calibrating parameters of the neural radiation field three-dimensional code space model. According to the method, the spatio-temporal characteristics of code change are analyzed, Monte Carlo simulation is combined, hidden dependence and a risk conduction chain are identified, and the comprehensiveness and predictive ability of framework corruption analysis are improved.

The invention discloses an unbiased scene graph generation method for relieving long-tail distribution. The method comprises the following steps: S1, constructing a model; s2, data preprocessing; s3, object feature extraction; s4, constructing a graph learning structure (GLS); s5, a regional message passing network (RMPN); s6, generating a pseudo label; s7, defining a loss function; s8, performing model training; s9, generating a pseudo tag and a triple; and S10, carrying out iterative training and optimization. According to the unbiased scene graph generation method for relieving long-tail distribution, the correlation between entities is calculated by utilizing GLS, the relation graph is optimized, the relation representation of head and tail categories is enhanced, meanwhile, the RMPN improves the semantic representation of objects and relations through an information transmission mechanism, pseudo labels are generated on the basis of unlabeled relations in a training set through a pseudo label generation mechanism, and the robustness of the unbiased scene graph generation method for relieving long-tail distribution is improved. And in combination with a high-confidence screening mechanism, generating a learning sample of a pseudo-triple enhanced tail category.